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9th International Conference on Education and Technology, ICET 2023

作者： Gunawan, Imam Nurabadi, Ahmad Amalia, Nurul Hui, Lim Kim Nabila, Achmad Wildan Krisnafitriana, Virra Budiarti, Erika Mei Wardani, Adetya Dewi Universitas Negeri Malang Dept. of Educational Administration Malang Indonesia Universitas Negeri Malang Dept. of History Malang Indonesia Hankuk University of Foreign Studies Dept. of Malay-Indonesian Language Seoul Korea Republic of National Tsing Hua University Dept. of Industrial Engineering and Engineering Management Hsinchu Taiwan National Tsing Hua University Dept. of Taipei School of Economics and Political Science Hsinchu Taiwan Universitas Hasyim Asy'ari Dept. of Islamic Education Management Jombang Indonesia Sma Islam Sabilurrosyad Malang Malang Indonesia

ISBN: (纸本)9798350358292

This research was conducted using bibliometric historiography with a visualization approach to comprehensively understand the evolution of the field of e-Leadership. The data source of this research is a database obtained from the Web of science, a total of 185 documents from 1997 to 2022. The software used to analyze the data is HistCite. Descriptive statistical analysis and bibliometric historiography are used to determine development trends in the field of e-Leadership. This study presents a significant paper, which is categorized as a paper that has a major influence on the development of the field of e-Leadership, based on the Local Citation Score. Practical and theoretical implications are discussed, and future research possibilities have also been suggested. © 2023 ieee.

关键词： bibliometric historiography e-Leadership electronic leadership HistCite leadership

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Cloud-Based Processing on Drone Imaging for Precision Agriculture 14

Cloud-Based Processing on Drone Imaging for Precision Agricu...

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14th ieee International Conference on Software Engineering and Service science, ICSESS 2023

作者： Cam, Joseph Ramses Mendez De La Cruz, Eulogio Guillermo Santos Lopez, Felix Melchor Santos Urbano, Victor Genaro Rosales School of Science and Engineering Pontifical Catholic University of Peru Lima Peru National University of San Marcos Faculty of Industrial Engineering Lima Peru Pontifical Catholic University of Peru Department of Engineering Lima Peru

ISBN: (纸本)9798350336269

The agricultural industry plays a crucial role in society and consistently invests large quantities of money. This is because the process of growing crops involves varied processes that are often time-consuming. In most countries, manual labor is still a key component of agriculture. However, with current technologies, many processes in precision agriculture can be automated or improved. Among these is traditional plant health inspection, which is often time-consuming and inefficient. The research gap this paper is trying to handle is about the difficult task of continuously monitoring crops. This paper aimed to address this challenge by proposing an innovative solution for assessing plant health, which applied cloud-based processing for aerial images. It was inspired by existing drone projects, as well as the Internet of Things technology. It used data captured from drone sensors and processed it automatically in the cloud. The software architecture used to achieve this was guided by the Attribute-Driven Design (ADD) methodology. According to the findings of the study, 430 ms was the average response time for plant state classification. Moreover, the cloud architecture was capable of sending an alarm if an unusual state was reached. Finally, a colored map was created to enable better visualization. © 2023 ieee.

关键词： Drones

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The Use of Administrative data to Generate Information for Decision-Making in Healthcare: A Narrative Review 11

The Use of Administrative Data to Generate Information for D...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Khotimah, Purnomo Husnul Natari, Rifani Bhakti Riswantini, Dianadewi Research Center for Data & Information Science National Research & Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Information technology has recently led to vast data generation, including in healthcare services. This data can be used to generate evidence for decision-making and has been widely used in high-income countries (HICs) to inform their decision-making process. However, the use of data in low- and middle-income countries (LMICs) is lacking. The burden of diseases between HICs and LMICs differs, with LMICs still struggling to battle infectious diseases. Thus, we aim to review the available literature on the status of using administrative data in generating evidence and how to use this data for decision-making on infectious diseases. Most studies were conducted in HICs, with the majority in Taiwan, the United States, South Korea, France, and Japan, compared to LMICs. Research in LMICs is scarce, highlighting inadequate information on prevalent diseases in LMICs, but not in HICs, such as neglected tropical diseases. HICs’ research utilized data from insurance claims, electronic medical records, and linkage from various databases. Administrative data are used for five main purposes, as follow 1.) to describe the epidemiology of diseases, 2.) to identify risk factors or associations among diseases, 3.) to evaluate healthcare services or policies, 4.) to identify any temporal changes in diseases, and 5.) to improve the validity of studies. The methodological approaches are diverse, ranging from regression analysis to advanced analysis such as machine learning. Yet, machine learning's potential in this domain remains underutilized since the applied analyses are mostly limited to data visualization, and conventional machine learning such as XGBoost, and decision tree analysis. An important highlight is that there are still gaps and opportunities for LMICs to further explore insights for infectious disease management, due to the current lack of data science research from LMICs in this area. © 2024 ieee.

关键词： Electronic health record

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Analysis of the Surface Water Quality in the State of Karnataka using Distributed Platform 9

Analysis of the Surface Water Quality in the State of Karnat...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Dasu, Shravya Shobha, G. Shetty, Jyothi RV College of Engineering Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Water, one of the essential substances for life on earth is crucial for humans, plants and animals. Regular testing and monitoring of water quality is essential to ensure its safety for consumption. This process includes measuring physical and chemical parameters such as PH, total dissolved solids (TDS) etc. Herein, surface water quality in Karnataka, India was analyzed over 40 years. In order to analyze the large amount of data collected, high performance computing cluster (HPCC) systems was used. Biochemical oxygen demand (BOD) predictive model was built using linear regression. Additionally, a potability classification model was built using the random forest classifier algorithm. These algorithms are present in the LinearRegression and LearningTrees bundle provided by HPCC Systems. Finally, a graphical visualization of water quality in four basins over time was performed with the Visualizer bundle. This was done to understand the improvement or degradation of water quality. The analysis was performed on a single node and an 8-node cluster respectively. In comparison with the former, the latter had a shorter execution time. © 2023 ieee.

关键词： Water quality

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Framework of Decision Support System for Effective Resource Management 2

Framework of Decision Support System for Effective Resource ...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Naz, Muhammad Imran Rajper, Samina Ali, Atif Khan, Muhammad Faisal Asif, Rabia Mehmood, Muhammad Hassaan Sahawneh, Nizar Kalra, Deepak Shah Abdul Latif University Sindh Pakistan Khairpur Pakistan Cyberjaye63100 Malaysia Riphah International University Islamabad Pakistan NCBA&E Dept of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Jordan Skyline University College School of Business Sharjah United Arab Emirates University City Sharjah School of Information Technology Skyline University College Sharjah1797 United Arab Emirates

ISBN: (纸本)9798350335644

A framework of decision support system for effective resource management typically includes the data Collection (Gathering relevant data on resources, such as availability, capacity, and utilization), data Analysis (Analyzing the collected data to identify trends and patterns in resource utilization), Modelling (Developing mathematical models to support resource allocation and decision-making), Optimization (Finding the most efficient way to allocate resources to meet project requirements), visualization (Presenting the results of the analysis and optimization in a clear and concise manner), Integration (Integrating the decision support system with other relevant systems, such as project management and enterprise resource planning systems), Feedback (Collecting feedback from stakeholders and incorporating it into the decision support system to improve its effectiveness), Continual Improvement (Regularly reviewing and improving the decision support system to ensure it remains effective) © 2023 ieee.

关键词： Decision support systems

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BlockAgro: Towards Blockchain assisted IoST Framework for Agricultural Sector

BlockAgro: Towards Blockchain assisted IoST Framework for Ag...

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2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Mallick, Soubhagya Ranjan Apat, Hemant K. Lenka, Rakesh Kumar Ranjan Senapati, Manas Sharma, Suraj Barik, Rabindra K. Iiit Department of Cse Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Trident Academy of Technology Department of Chemistry & Environmental Science Bhubaneswar India Guru Ghasidas Vishwavidyalaya Central University Department of Cse Bilaspur India

ISBN: (纸本)9798350338003

Blockchain and Internet of Spatial Things (IoST) integration in smart agricultural systems employing Geographical Information System (GIS) provides agricultural systems with decentralized, trustworthy, secure, dependable, scalable, traceable, and transparent services. Most previous research uses IoT to automate agricultural systems without addressing security, scalability, single-point failure, access control, trust, and transparency concerns. With an expanded marketplace, distributors, producers, buyers, sellers, and other agricultural users, the previously proposed cloud-based and centralized agricultural systems faced limits in data collection, sharing, management, processing, storing, availability, security, auditability, and stability. To address the challenges that exist in IoST agricultural systems, we propose BlockAgro, a Blockchain-assisted IoST framework for the agricultural sector, with a three-tier architecture, namely an IoST device layer, a fog layer, and a cloud layer, to address the issues in IoT agricultural systems. Additionally, it provides data analysis, visualization, processing, filtration, and temporary storage of gathered IoST data at the nearest fog nodes to reduce cloud infrastructure overhead. Moreover, the experimental findings of the proposed framework ensure the access control mechanism for IoST devices, efficiency measurement metrics, and data availability in a decentralized agricultural platform. © 2023 ieee.

关键词： Blockchain

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Lightweight Single-Stage Network for Gas Leak Detection Based on Infrared Imaging

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Jing, Yixuan Sun, Yunlong Wang, Qi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Hebei Key Lab Micro Nano Precis Opt Sensing & Meas Qinhuangdao 066004 Peoples R China

Gas leak detection is essential for real-time monitoring and safety early warning of industrial production, manufacturing, and transportation processes. For many years, infrared optical gas imaging (IOGI) has been widely used in the field of gas leak monitoring, but the task still faces great challenges due to the limitations of infrared imaging principle and system technology, as well as the characteristics of insubstantial gas objects. First, a dataset containing 66950 infrared images is built, which covers gas leak samples with different scales, shapes, and blurring levels. Second, a single-stage gas leak detection network model named dual layer focus aggregation network (DLFANet) was designed. Specifically, a lightweight feature extraction cross-stage partially efficient two-layer aggregation network cross stage partial-efficient dual layer aggregation network (CSP-EDLAN) module is designed to enhance the transmission of gradient flow information and cross-channel information interaction, where dual convolution (DualConv) is utilized to reduce the computational consumption of feature extraction. A focal modulation module is introduced into the backbone network to realize the focus of the gas target by integrating the characteristic information of different scales. In addition, the wise intersection shape intersection over union (Wise-Shape-IoU) loss function with a dynamic non-monotonic mechanism and shape constraint capability is designed to prevent low-quality samples from generating harmful gradients, which makes the bounding box regression (BBR) of gas targets with greater accuracy. Finally, extensive experimental results on the constructed dataset show that the proposed DLFANet strikes a better balance between detection accuracy (map) and speed frame per second (FPS) while predicting the BBR of gaseous objects more accurately compared to state-of-the-art models.

关键词： Feature extraction Modulation Shape Training data mining visualization Leak detection Infrared imaging Accuracy Real-time systems Deep learning industrial gas leakage infrared imaging insubstantial gas object detection

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A Robust Convolutional Neural Network Approach for Classifying Skin Cancer 1

A Robust Convolutional Neural Network Approach for Classifyi...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Ranjith Kumar, M. Kumar, G V Krishna Nikhil, V. Ishwariya, R. Govindan, Vediyappan Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India DMI-St. John the Baptist University Mangochi Department of Mathematics Malawi

ISBN: (纸本)9798350330915

Early detection identified by dermoscopy images significantly decreases the mortality rate from skin cancer. However, the accuracy of the system diagnosis is impacted by multiple factors. A significant issue in this procedure arises during the process of acquiring images. The image quality in medical photography is frequently affected by unanticipated circumstances such as noises and brightness variations, initial digitalization, and sampling. In this work, we suggest a method to reduce the possibility of erroneous diagnosis. The proposed approach begins with preprocessing the data set through CNN and deep learning techniques for data visualization, pre-processing, and augmentation. This preprocessing aids in accelerating the rate of reorganization by eliminating all irrelevant textures. The outcomes identified a 96% accuracy rate with a 4% margin of error. This scheme demonstrated a high degree of accuracy in determining the images, as evidenced by its Precision and F1 Scores of 85% and 87%, respectively. © 2023 ieee.

关键词： Carcinoma Convolutional Neural Networks Deep Learning Machine Learning Skin Cancer

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Automated Detection of Glaucoma from Fundus Images Using Deep Convolutional Networks 10

Automated Detection of Glaucoma from Fundus Images Using Dee...

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10th ieee Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Yadav, Kanchan Sharma, Manish Deepak, A. Mayuri, K. Yadav, Vijay Kumar Kumar, Navneet Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

This study uses Convolutional Neural Networks on retinal images to meet the critical requirement for precise and understandable automated glaucoma identification. Utilizing secondary data collected from credible ophthalmic databases, a deductive strategy was used within a descriptive study framework while adopting an interpretive mindset. Through meticulous training on a varied dataset that included both healthy patients and cases with various stages of glaucoma, the design of CNN was refined. With a precision of 92% as well as a specificity rating of 94%, its CNN-based myopia detection system demonstrated outstanding diagnostic accuracy. Additionally, the model proved to be robust across a range of demographic groupings, guaranteeing consistent results regardless of age, sexual orientation, or ethnicity. In particular, the approach demonstrated exceptional adaptability to differences in disease delivery, excelling especially the identification of the initial stages glaucoma. Features of interpretability were added to increase decision-making visibility. Insights about the algorithm's diagnostic procedure were gained through the use of saliency maps members' feature visualization approaches, which enabled doctors and the computer program to make decisions together. Although the system shows promising findings, more study is advised to evaluate its performance in actual clinical situations and to improve interpretability methods. Automated glaucoma detection needs to continue to progress, and this requires prospective data collection initiatives and adherence to strict ethical standards. This study sets the groundwork for a highly useful and understandable instrument that will transform the early detection and treatment of glaucoma © 2023 ieee.

关键词： blindness worldwide glaucomatous eyes hallmark neural network models potentially revolutionize

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Spatial Characteristics Analysis and visualization for Brain Networks of Motor Imagery Based on Graph Convolutional Neural Networks 17

Spatial Characteristics Analysis and Visualization for Brain...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Pan, Yuhang Xie, Li Wang, Daming College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Zhejiang University School of Medicine First Affiliated Hospital Hangzhou China

ISBN: (纸本)9798331507398

As the most complex organ in the human body, the study of the human brain has always been a hot topic in the field of neuroscience. This paper mainly studies the relevant analysis and visualization of the spatial characteristics of the brain networks on motor imagery. In view of the fact that graph convolutional neural networks can better process graph structure data in non-Euclidean space and will not lose the coupling information between EEG signal channels, they are chosen as the model structure. We also study different graph convolutional neural network structures, and find the model structure with the best classification effect by adjusting the network structure and comparing the effects of different models. In addition, we evaluated the optimal training model in many aspects. Finally, in order to observe the spatial characteristics of EEG signals more conveniently, comprehensively and from multiple levels, we designed a functional and user-friendly visualization software based on the results, which displays the diverse information of raw EEG signals and brain networks. © 2024 ieee.

关键词： Convolutional neural networks

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